Classic Hebbian learning endows feed-forward networks with sufficient adaptability in challenging reinforcement learning tasks

A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability post-optimization. Najarro & Risi (2020) demonstrate how such adaptability may be salvaged in artificial feed-forward networks by optimizing coefficients of classic Hebbian rules to dynamically control the networks' weights instead of optimizing the weights directly. While such models fail to capture many important neurophysiological details, allying the fields of neuroscience and artificial intelligence in this way bears many fruits for both fields, especially when computational models engage with topics with a rich history in neuroscience such as Hebbian plasticity.

Payments Systems in the NHS

Finance within the NHS is poorly understood by clinicians and the limited engagement of clinical leaders in finance decisions is recognised as a common pitfall in recent inquiries such as the Francis report1 and recent King’s Fund report into financial failure2. The complexity and rapidly changing landscape of financing systems in place across the NHS on a background of complex and transient organisational structure makes it challenging for the patient focused clinician to achieve a working knowledge of finance. This article will discuss various payment mechanism and explore their advantages and disadvantages and then discuss how they have been used on their own or in combination to fund healthcare organisations. In order to provide context for clinicians, we will discuss how the payment contracts influence day to day health service practice.